How to Use Claude Code with Ollama’s Anthropic API

In November 2025, Ollama’s groundbreaking integration with Anthropic’s API has revolutionized local LLM development by enabling seamless use of Claude Code with open-source models. This hybrid approach combines the power of Anthropic’s advanced reasoning capabilities with Ollama’s lightweight local deployment, offering developers unprecedented privacy and flexibility. Whether you’re building complex applications or debugging intricate codebases, this integration provides a game-changing solution for modern development workflows.

Understanding the integration architecture

Ollama’s Anthropic API compatibility layer creates a bridge between local model execution and cloud-based capabilities through a sophisticated proxy system. This architecture maintains strict security boundaries while enabling developers to leverage Claude Code’s advanced coding assistance features like code generation, debugging, and documentation. The system operates through three core components:

Key components

• Local Ollama Server – Manages model execution and resource allocation on the developer’s machine
• Anthropic API Proxy – Handles secure communication between local environment and cloud services
• Model Gateway – Enables dynamic switching between local and cloud-based models

Getting started with setup

Before diving into code, ensure you have these prerequisites installed:

• Ollama 3.5 (latest version as of November 2025)
• Python 3.11+ with pip
• Anthropic API key (free tier available)
• Git for version control

Follow these steps to prepare your environment:

1. Download and install Ollama from ollama.com
2. Verify installation with ollama --version
3. Install the Anthropic Python SDK: pip install anthropic
4. Set your API key: export ANTHROPIC_API_KEY='your-key'

Configuring Ollama for Anthropic API

Create a configuration file at ~/.ollama/config.json with the following settings:

{
  "anthropic": {
    "api_key": "your-anthropic-api-key",
    "proxy": {
      "enabled": true,
      "port": 11434
    },
    "models": {
      "claude-3-5-sonnet": {
        "max_tokens": 8192,
        "temperature": 0.2
      }
    }
  }
}

Start the Ollama server with Anthropic integration:

ollama serve --anthropic

Using Claude Code with Ollama

Now you can use Claude Code through the Ollama interface. Here’s a practical example demonstrating code generation:

Create a Python script generate_code.py with:

import anthropic

client = anthropic.Anthropic(
    base_url="http://localhost:11434",
    api_key="ollama",
)

response = client.messages.create(
    model="claude-3-5-sonnet",
    max_tokens=1000,
    messages=[
        {
            "role": "user",
            "content": "Create a Python function that calculates Fibonacci numbers efficiently"
        }
    ]
)

print(response.content[0].text)

Advanced configuration options

For specialized use cases, customize your setup using these configuration parameters:

Troubleshooting common issues

If you encounter connection problems between Ollama and Anthropic API:

• Verify API key format and permissions
• Check network connectivity to Anthropic servers
• Ensure Ollama version matches API requirements
• Review logs at ~/.ollama/logs/anthropic-proxy.log

Conclusion and next steps

Ollama’s Anthropic API integration represents a significant leap in local LLM development capabilities. By combining Claude Code’s advanced coding assistance with Ollama’s efficient local execution, developers gain enhanced privacy, reduced latency, and flexible deployment options. To get started:

• Install Ollama 3.5 and configure the Anthropic API
• Experiment with code generation and debugging workflows
• Explore advanced configuration options for specific use cases

As Anthropic continues expanding its API capabilities and Ollama adds new features, this integration will become increasingly powerful for developers seeking to maintain control over their code while leveraging cutting-edge AI assistance. The future of code development is clearly moving towards this hybrid model of local execution with cloud capabilities, and now you have the tools to start building with it today.